Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office action is in response to Applicant’s amendment submitted on May 12, 2026.
Claims 1-2, 4-20 are pending in the application.
Response to Arguments/Remarks
Specification
The specification was objected to because the title of the invention was not descriptive. Applicant has filed a new title, which has addressed the objection. Accordingly, the objection has been withdrawn.
Claim Interpretation
Claim limitation(s) were interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Claim 1 has been amended to specify “a memory configured to store a computer program; and processor circuitry coupled to the memory and configured to execute the computer program to:” perform steps of the claims. Accordingly, the limitations are no longer interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph
Claim Rejections - 35 USC § 112
Claims 2-14, 16-18 were rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
Applicant’s amendments did not overcome every rejection. Furthermore, the amendments have necessitated new grounds of rejections. See below.
Claim Rejections - 35 USC § 102
Claims 1-2, 4-6, 8-10, 19-20 were rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shen et al. US Patent Publication No. 2022/0342713 (“Shen”).
Claim 3 was rejected under 35 U.S.C. 103 as being unpatentable over Shen in view of Kumar et al. US Patent Publication No. 2022/0150125 (“Kumar”) and Fan US Patent Publication No. 2024/0236826 (“Fan”).
Applicant argued that the combination fails to disclose that a terminal equipment is configured to query existence of specific AI/ML models, and accordingly, the cited art fails to disclose "the processor circuitry [in a terminal equipment] is further configured to: query whether there exist matching AI/ML models in the terminal equipment.
The examiner respectfully disagrees that the Shen and Kumar do not teach the limitations. Shen already discloses a terminal equipment configured to store AI/ML models and query existence of AI/ML models in response to a capability request. Shen, on paragraph [0078] and [0080], discloses, “terminal sends artificial intelligence (AI)/machine learning (ML) capability information to a network device” and “when the network device requests the terminal to report the AI/ML capability information, the terminal reports the AI/ML capability information.” Shen, on paragraph [0079], discloses, “the AI/ML capability information may further include a serial number of a currently stored AI/ML model.” Shen, on paragraph [0112], discloses, “list of AI/ML models stored in the terminal includes identity information such as serial numbers and names of AI/ML models currently stored in the terminal.” Therefore, Shen discloses a terminal equipment that is configured and capable of querying existence of AI/ML models in the terminal equipment. However, Shen does not teach the request including a model identification and the terminal equipment matching an AI/ML model according to the model identification.
Kumar discloses a capability query request that includes an AI/ML model group identification and/or a model identification and/or a version identification, and a processor circuitry configured to query whether there exist matching AI/ML models according to the AI/ML model group identification and/or the model identification and/or the version identification supported by the network device, and include positive information of the model group identification and/or the model identification and/or the version identification in the capability query response or report in a case where there exist consistent AI/ML models (para. [0064] request for the AI model from the example service 405 can include information such as an AI-NF inference with a named model. para. [0116] AI-NF logic circuitry 430-434 can also query its local AI-NF inventory circuitry 440-444 and/or the model inventory circuitry 550, the model cache 460, etc., in response to a request from the service. para. [0118] selected AI model (or an instance of the selected AI model) is then made available to the requestor. deploys an instance of the selected model to the requestor and/or makes the selected model available for execution). While Kumar’s disclosure of matching is performed by a device such as an edge device, the functionality to query and match model identification is still taught by Kumar and thus known in the art. Shen discloses a terminal equipment configured to store and query existence of AI/ML models.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 4, 9, and 11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 2, there is insufficient antecedent basis for “the AI/ML model.” Claim 1 has been amended to recite the limitations, “an AI/ML model group identification,” “a model identification,” and “AI/ML models.” Claim 2 also recites “an AI/ML model group identification” and “an AI/ML model identification.” The limitations refer to identifications and/or models. However, the claims do not specify any particular “AI/ML model” for the “AI/ML model.”
Regarding claim 4, it is not clear which AI/ML model, “the AI/ML model,” is referring to because the claim has been amended to recite “an identification of an AI/ML model supported by the terminal equipment” and “an AL/ML model transmitted by the network device.”
Regarding claim 9, the claim recites, “wherein an identification related to an AI/ML model includes…” The claim attempts to further define identification related to the AI/ML model. However, the claims 1 and 9 do not provide any prior recitation for “an identification related to an AI/ML model.” The limitation “an identification” recited in claim 9 is not limited to the apparatus of claim 1. Therefore, it is not clear whether “an identification related to an AI/ML model” is required by the claim. See MPEP 2111.04. Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure.
Regarding claim 11, it is not clear which AI/ML model “the AI/ML model” is referring to because the claim has been amended to recite “an AI/ML model,” “an AI/ML model transmitted by the network device,” and “an AI/ML model according to the indication information.” It is noted that “the AI/ML model” is recited more than once in the claim.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-2, 4-10, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. US Patent Publication No. 2022/0342713 (“Shen”) in view of Kumar et al. US Patent Publication No. 2022/0150125 (“Kumar”) and Fan US Patent Publication No. 2024/0236826 (“Fan”).
Regarding claim 1, Shen teaches an information transmission apparatus, configured in a terminal equipment, comprising:
a memory configured to store a computer program; and
processor circuitry coupled to the memory and configured to execute the computer program to:
receive a capability query request of AI/ML transmitted by a network device (para. [0080] when the network device requests the terminal to report the AI/ML capability information, the terminal reports the AI/ML capability information.); and
feed back a capability query response or report to the network device according to the capability query request (para. [0085] terminal sends artificial intelligence (AI)/machine learning (ML) capability information to a network device),
processor circuitry configured to query whether there exist AI/ML models in a terminal equipment, and include positive information of the model group identification and/or the model identification and/or the version identification in the capability query response or report (para. [0078] terminal sends artificial intelligence (AI)/machine learning (ML) capability information to a network device para. [0079] the AI/ML capability information may further include a serial number of a currently stored AI/ML model).
Shen does not teach:
wherein the capability query request includes an AI/ML model group identification and/or a model identification and/or a version identification for a certain signal processing function supported by the network device, and the apparatus further comprising:
processor circuitry is further configured to query whether there exist matching AI/ML models in a terminal equipment according to the AI/ML model group identification and/or the model identification and/or the version identification supported by the network device, and include positive information of the model group identification and/or the model identification and/or the version identification in the capability query response or report in a case where there exist matching AI/ML models.
Kumar discloses an apparatus configured to receive a capability query request, wherein the capability query request that includes an AI/ML model group identification and/or a model identification and/or a version identification, and the apparatus comprising: processor circuitry configured to query whether there exist matching AI/ML models according to the AI/ML model group identification and/or the model identification and/or the version identification supported by the network device, and include positive information of the model group identification and/or the model identification and/or the version identification in the capability query response or report in a case where there exist consistent AI/ML models (para. [0064] request for the AI model from the example service 405 can include information such as an AI-NF inference with a named model. para. [0116] AI-NF logic circuitry 430-434 can also query its local AI-NF inventory circuitry 440-444 and/or the model inventory circuitry 550, the model cache 460, etc., in response to a request from the service. para. [0118] selected AI model (or an instance of the selected AI model) is then made available to the requestor. deploys an instance of the selected model to the requestor and/or makes the selected model available for execution. makes the selected model available for execution to provide an output/outcome to the requestor). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shen with Kumar’s disclosure such that the capability request received by the apparatus of Shen includes a model identification and the apparatus is further configured to determine a match of an AI/ML model with the model identification. One of ordinary skill in the art would have been motivated to do so because Shen discloses querying for capability information, and it would have been desirable to have provided the capability for a device query a specific model and deployment of a model that satisfies the query.
Fan discloses an AI model for a certain signal processing function (para. [0026] AI model is configured to implement signal modulation and demodulation. AI model is configured to implement encoding and decoding of a signal. para. [0063] first message may carry a service identifier corresponding to at least one type of model data.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shen with Fan’s disclosure of implementing an AI model for signal processing function. One of ordinary skill in the art would have been motivated to do so because Shen disclose using the models to perform tasks, and it would have been beneficial to provide a model capable of performing additional tasks including the encoding and decoding.
Regarding claim 19, Shen teaches an information transmission apparatus, comprising:
a memory configured to store a computer program; and
processor circuitry coupled to the memory and configured to execute the computer program to:
transmit a capability query request of AI/ML to a terminal equipment (para. [0080] when the network device requests the terminal to report the AI/ML capability information, the terminal reports the AI/ML capability information.); and
receive a capability query response or report fed back by the terminal equipment according to the capability query request (para. [0085] terminal sends artificial intelligence (AI)/machine learning (ML) capability information to a network device),
wherein capability query response or reports positive information of the model group identification and/or the model identification and/or the version identification in the capability query response or report where there exist AI/ML models in a terminal equipment (para. [0078] terminal sends artificial intelligence (AI)/machine learning (ML) capability information to a network device para. [0079] the AI/ML capability information may further include a serial number of a currently stored AI/ML model).
Shen does not teach:
wherein the capability query request includes an AI/ML model group identification and/or a model identification and/or a version identification for a certain signal processing function supported by the network device, and
the capability query response or report includes positive information of the model group identification and/or the model identification and/or the version identification in the capability query response or report in a case where there exist matching AI/ML models according to the AI/ML model group identification and/or the model identification and/or the version identification supported by the network device.
Kumar discloses an apparatus configured to transmit a capability query request, wherein the capability query request includes an AI/ML model group identification and/or a model identification and/or a version identification for a certain signal processing function supported by the network device, and the capability query response or report includes positive information of the model group identification and/or the model identification and/or the version identification in the capability query response or report in a case where there exist matching AI/ML models according to the AI/ML model group identification and/or the model identification and/or the version identification supported by the network device (para. [0064] request for the AI model from the example service 405 can include information such as an AI-NF inference with a named model. para. [0116] AI-NF logic circuitry 430-434 can also query its local AI-NF inventory circuitry 440-444 and/or the model inventory circuitry 550, the model cache 460, etc., in response to a request from the service. para. [0118] selected AI model (or an instance of the selected AI model) is then made available to the requestor. deploys an instance of the selected model to the requestor and/or makes the selected model available for execution). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shen with Kumar’s disclosure such that the capability request received by the apparatus of Shen includes a model identification and the apparatus is further configured to determine a match of an AI/ML model with the model identification. One of ordinary skill in the art would have been motivated to do so because Shen discloses querying for capability information, and it would have been desirable to have provided the capability to query a specific model and enabled deployment of a model that satisfies the query.
Fan discloses an AI model for a certain signal processing function (para. [0026] AI model is configured to implement signal modulation and demodulation. AI model is configured to implement encoding and decoding of a signal. para. [0063] first message may carry a service identifier corresponding to at least one type of model data.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shen with Fan’s disclosure of implementing an AI model for signal processing function. One of ordinary skill in the art would have been motivated to do so because Shen disclose using the models to perform tasks, and it would have been beneficial to provide a model capable of performing additional tasks including the encoding and decoding.
Regarding claim 20, Shen teaches a communication system, comprising:
a terminal equipment including first processor circuitry; and a network device including second processor circuitry,
the first processor circuitry being configured to receive a capability query request of AI/ML, and feed back a capability query response or report according to the capability query request (para. [0080] when the network device requests the terminal to report the AI/ML capability information, the terminal reports the AI/ML capability information. terminal reports the AI/ML capability information. para. [0085] terminal sends artificial intelligence (AI)/machine learning (ML) capability information to a network device); and
the second processor circuitry being configured to transmit the capability query request of AI/ML, and receive the capability query response or report (para. [0080] when the network device requests the terminal to report the AI/ML capability information. para. [0085] terminal sends artificial intelligence (AI)/machine learning (ML) capability information to a network device),
wherein the first processor circuitry in the terminal equipment further configured to query whether there exist AL/ML models in the terminal equipment, and include positive information of the model group identification and/or the model identification and/or the version identification in the capability query response or report where there exist AI/ML models (para. [0078] terminal sends artificial intelligence (AI)/machine learning (ML) capability information to a network device para. [0079] the AI/ML capability information may further include a serial number of a currently stored AI/ML model).
Shen does not teach:
wherein the capability query request includes an AI/ML model group identification and/or a model identification and/or a version identification for a certain signal processing function supported by the network device, and
the first processor circuitry in the terminal equipment configured to query whether there exist matching AI/ML models in the terminal equipment according to the AI/ML model group identification and/or the model identification and/or the version identification supported by the network device, and include positive information of the model group identification and/or the model identification and/or the version identification in the capability query response or report in a case where there exist matching AI/ML models.
Kumar discloses an apparatus configured to transmit a capability query request, wherein the capability query request includes an AI/ML model group identification and/or a model identification and/or a version identification for a certain signal processing function supported by the network device, and the capability query response or report includes positive information of the model group identification and/or the model identification and/or the version identification in the capability query response or report in a case where there exist matching AI/ML models according to the AI/ML model group identification and/or the model identification and/or the version identification supported by the network device (para. [0064] request for the AI model from the example service 405 can include information such as an AI-NF inference with a named model. para. [0116] AI-NF logic circuitry 430-434 can also query its local AI-NF inventory circuitry 440-444 and/or the model inventory circuitry 550, the model cache 460, etc., in response to a request from the service. para. [0118] selected AI model (or an instance of the selected AI model) is then made available to the requestor. deploys an instance of the selected model to the requestor and/or makes the selected model available for execution). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shen with Kumar’s disclosure such that the capability request received by the apparatus of Shen includes a model identification and the apparatus is further configured to determine a match of an AI/ML model with the model identification. One of ordinary skill in the art would have been motivated to do so because Shen discloses querying for capability information, and it would have been desirable to have provided the capability to query a specific model and enabled deployment of a model that satisfies the query.
Fan discloses an AI model for a certain signal processing function (para. [0026] AI model is configured to implement signal modulation and demodulation. AI model is configured to implement encoding and decoding of a signal. para. [0063] first message may carry a service identifier corresponding to at least one type of model data.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shen with Fan’s disclosure of implementing an AI model for signal processing function. One of ordinary skill in the art would have been motivated to do so because Shen disclose using the models to perform tasks, and it would have been beneficial to provide a model capable of performing additional tasks including the encoding and decoding.
Regarding 2, Shen in view of Kumar and Fan teach the apparatus according to claim 1. Shen teaches, wherein the capability query request comprises querying at least one of the following:
an AI/ML capability; a certain signal processing function; an AI/ML model group identification; an AI/ML model identification; a version identification of the AI/ML model; an update capability of the AI/ML model; a performance monitoring capability or performance evaluation capability of the AI/ML model; a training capability of the AI/ML model; or a storage capability related to update of the AI/ML model (para. [0080] when the network device requests the terminal to report the AI/ML capability information.);
wherein the capability query response or report includes at least one of the following: whether an AI/ML capability is supported; whether a certain signal processing function is supported; whether a queried AI/ML model group identification is supported, or, a supported AI/ML model group identification; whether a queried AI/ML model identification is supported, or a supported AI/ML model identification; whether a version identification of a queried AI/ML model is supported, or a version identification of a supported AI/ML model; whether an update capability of the AI/ML model is supported; whether a performance monitoring capability or performance evaluation capability of the AI/ML model is supported; whether a training capability of the AI/ML model is supported; or a storage capability related to update of the AI/ML model (para. [0079] AI/ML capability information indicates the resource information, performance index requirement of wireless transmission, a type of stored training data, AI/ML capability information may further include a serial number of a currently stored AI/ML model. para. [0085] terminal sends artificial intelligence (AI)/machine learning (ML) capability information to a network device).
Regarding claim 4, Shen in view of Kumar and Fan teach the apparatus according to claim 1. Shen teaches wherein the capability query response or report includes updated capability information and/or an identification of an AI/ML model supported by the terminal equipment, and the processor circuitry is further configured to receive update information of an AI/ML model transmitted by the network device (para. [0079] AI/ML capability information may further include a serial number of a currently stored AI/ML mode. para. [0080] terminal can periodically report the AI/ML capability information to the network device. para. [0083] network device can flexibly switch an AI/ML model run by the terminal. distribute a suitable AI/ML model to the terminal);
wherein the update information of the AI/ML model includes a parameter or identification of the AI/ML model, and the terminal equipment selects a corresponding AI/ML model according to the parameter or identification, or downloads a corresponding AI/ML model from a core network device or the network device (para. [0089] when the terminal has a great available computing power, a larger AI/ML model can be run by the terminal. when the AI/ML model run by the terminal varies, a model run by the network device also varies. network device can select an AI/ML model suitable for the terminal according to the AI/ML task of the terminal).
Regarding claim 5, Shen in view of Kumar and Fan teach the apparatus according to claim 1. Shen teaches wherein the capability query response or report includes capability information on whether the terminal equipment supports training, and/or capability information on whether the terminal equipment supports performance evaluation indicated by a network (para. [0079] AI/ML capability information includes… a performance index requirement on wireless transmission of a network side by an AI/ML operation… a type of stored training data).
Regarding claim 6, Shen in view of Kumar and Fan teach the apparatus according to claim 1. Shen teaches wherein in a case where AI/ML model groups and/or signal processing functions supported by the terminal equipment and the network device are matching, the processor circuitry is further configured to receive an intra-group identification and/or a model identification of the AI/ML model groups transmitted by the network device (para. [0088] AI/ML task configuration information…. an AI/ML model. an identity of an AI/ML operation to be performed by the terminal, a serial number of an AI/ML act to be performed by the terminal. para. [0125] identity of the AI/ML model needed by the terminal to process the AI/ML service. para. [0135] AI/ML task configuration information includes the identity of the AI/ML model needed by the terminal to process the AI/ML service, and/or the AI/ML task configuration information includes an identity of an AI/ML act group to be performed by the terminal).
Regarding claim 7, Shen in view of Kumar and Fan the apparatus according to claim 1. Shen teaches wherein the processor circuitry is further configured to receive configuration information of the network device for processing, the configuration information including an identification of an AI/ML model group and/or an identification of an AI/ML model, and performs processing by using the AI/ML model corresponding to the identification of an AI/ML model group and/or the identification of the AI/ML model (para. [0092] terminal receives the AI/ML task configuration information sent by the network device. para. [0115]-[0123] AI/ML task configuration information includes. Identity of an AI/ML task, identity of an AI/ML model, an AI/ML model). Shen discloses receives configuration information of the network device but not for a certain signal processing function and perform signal processing by using an AI/ML model.
Fan discloses performs signal processing by using AI/ML model (para. [0026] AI model is configured to implement signal modulation and demodulation. AI model is configured to implement encoding and decoding of a signal. para. [0063] first message may carry a service identifier corresponding to at least one type of model data.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shen with Fan’s disclosure of performing signal processing by using AI/ML model such that the task of Shen further includes task(s) of encoding and decoding of a signal. One of ordinary skill in the art would have been motivated to do so because Shen disclose using the models to perform tasks, and it would have been beneficial to provide a model capable of performing additional tasks including the encoding and decoding.
Regarding claim 8, Shen in view of Kumar and Fan teach the apparatus according to claim 1. Shen teaches wherein the processor circuitry is further configured to receive a message for configuring or activating or enabling an AI/ML model transmitted by the network device, and uses a corresponding AI/ML model according to the message; and/or the processor circuitry is further configured to receive a message for de-configuring or deactivating or disabling an AI/ML model transmitted by the network device, and stops a corresponding AI/ML model according to the message (para. [0083] network device can flexibly switch an AI/ML model run by the terminal, distribute a suitable AI/ML model to the terminal, adjust the AI/ML training parameters. para. [0127] network device can indicate the terminal to delete an AI/ML model which is not optimal for the AI/ML capability of the terminal).
Regarding claim 9, Shen in view of Kumar and Fan teach the apparatus according to claim 1. Shen teaches wherein an identification related to an AI/ML model includes at least one of the following: a signal processing function identification, a model group identification, a model identification, a model category identification, a model layer number identification, a model version identification, or a model size or storage size identification (para. [0079] AI/ML capability information may further include a serial number of a currently stored AI/ML model. para. [0181]-[0182] information of the AI/ML model stored in the terminal for the AI/ML service includes any of the following information: a list of AI/ML models stored in the terminal, list of AI/ML models newly added to the terminal).
Regarding claim 10, Shen in view of Kumar and Fan teach the apparatus according to claim 1. Shen teaches, wherein the network device and the terminal equipment have AI/ML models with identical identifications, and the AI/ML model of the network device and the AI/ML model of the terminal equipment have been jointly trained (para. [0089] distributes the AI/ML model. when the AI/ML model run by the terminal varies, a model run by the network device also varies. para. [0090] carries the AI/ML model distributed by the network device to the terminal. “federated learning”, the AI/ML task indication information carries the AI/ML training parameter arranged by the network device for the terminal. para. [0157] obtain a trained AI/ML model).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Shen in view of Kumar, Fan, and Zhang US Patent Publication No. 2023/0247416 (“Zhang”).
Regarding claim 11, Shen does not teach the apparatus according to claim 1, wherein in a case where the terminal equipment supports update of an AI/ML model and has an available memory, the processor circuitry is further configured to receive indication information for transmitting an AI/ML model transmitted by the network device, and receives an AI/ML model according to the indication information; wherein the received AI/ML model includes identification information related to the AI/ML model, an AI/ML model structure and parameter information, wherein the identification information related to the AI/ML model is transmitted via radio resource control signaling or an MAC CE, or is transmitted via a data channel, and the AI/ML model structure and the parameter information are transmitted via a data channel; wherein the indication information includes an AI/ML model identification and/or a version identification, and after receiving the AI/ML model, the transmitter transmits feedback information to the network device, the feedback information including the AI/ML model identification and/or the version identification.
Zhang teaches a terminal equipment that supports update of an AI/ML model and has an available memory, a receiver receives indication information for transmitting the AI/ML model transmitted by a network device, and receives the AI/ML model according to the indication information; wherein the received AI/ML model includes identification information related to the AI/ML model, an AI/ML model structure and parameter information, wherein the identification information related to the AI/ML model is transmitted via radio resource control signaling or an MAC CE, or is transmitted via a data channel, and the AI/ML model structure and the parameter information are transmitted via a data channel (para. [0015] transmit information on a machine learning algorithm and model to a UE. para. [0019] information for indicating that the information on the machine learning algorithm and model is transferred through at least one of a control channel and a data channel. para. [0021] information on at least one of a model structure and a model parameter); wherein the indication information includes an AI/ML model identification and/or a version identification (para. [0140] base station transmits signaling and data information to the UE, transmitting to the UE that a specific model structure and model parameter of the ML algorithm and model used by the UE. para. [0162] information to the base station. para. [0162]-[0168] ML algorithm and model. para. [0240] request to update the ML algorithm and model), and after receiving the AI/ML model, the transmitter transmits feedback information to the network device, the feedback information including the AI/ML model identification and/or the version identification (para. [0149] UE reports state information (e.g., instant state information) on the UE. para. [0154]-[0156] performance indicators of the ML algorithm and model being executed by the UE). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shen with Zhang’s disclosure. One of ordinary skill in the art would have been motivated to do so for similar benefits of providing and updating ML algorithm and model for a certain task or function based on evaluation of key performance indicators.
Claims 12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar, Fan, and Wu et al. US Patent Publication No. 2024/0313838 (“Wu”).
Regarding claim 12, Shen does not teach the apparatus according to claim 1, wherein there exists an AI encoder for channel state information in the terminal equipment, and there exists, in the network device, an AI decoder with an identification and/or a version matching with that/those of the AI encoder, and the terminal equipment further has an AI decoder matching with the AI decoder in the network device, and the terminal equipment performs performance monitoring and/or training via the AI encoder in the terminal equipment and the AI decoder in the terminal equipment.
Wu discloses an AI encoder for channel state information in a terminal equipment, and in a network device, an AI decoder with an identification and/or a version matching with that/those of the AI encoder, and the terminal equipment further has an AI decoder matching with the AI decoder in the network device, and the terminal equipment performs performance monitoring and/or training via the AI encoder in the terminal equipment and the AI decoder in the terminal equipment (fig. 13, para. [0096] machine learning techniques may be used by the wireless communications system 200 to support CSI compression schemes, which may include training an encoder (e.g., training an auto encoder, evaluating…, training a decoder. para. [0109] base station, UE. training an encoder, decoder. para. [0116] base station 105 may configure a UE 115 with an auto encoder… and an auto decoder (e.g., using an indication 255, to configure a CSI compression evaluation component 260 to perform evaluations…). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shen with Wu’s disclosure. One of ordinary skill in the art would have been motivated to do so in order to have provided compression schemes and utilized machine learning to support the compression schemes.
Regarding claim 14, Shen does not teach the apparatus according to claim 1, wherein there exists an AI encoder for channel state information in the terminal equipment, and there exists, in the network device, an AI decoder with an identification and/or a version matching with that/those of the AI encoder, and the network device further has an AI encoder consistent with the AI encoder in the terminal equipment, and the network device performs performance monitoring and/or training via the AI encoder in the network device and the AI decoder in the network device.
Wu discloses an AI encoder for channel state information in the terminal equipment, and in the network device, an AI decoder with an identification and/or a version matching with that/those of the AI encoder, and the network device further has an AI encoder consistent with the AI encoder in the terminal equipment, and the network device performs performance monitoring and/or training via the AI encoder in the network device and the AI decoder in the network device (fig. 13, para. [0096] machine learning techniques may be used by the wireless communications system 200 to support CSI compression schemes, which may include training an encoder (e.g., training an auto encoder, evaluating…, training a decoder. para. [0109] base station, UE. training an encoder, decoder. para. [0116] base station 105 may configure a UE 115 with an auto encoder… and an auto decoder (e.g., using an indication 255, to configure a CSI compression evaluation component 260 to perform evaluations…). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shen with Wu’s disclosure. One of ordinary skill in the art would have been motivated to do so in order to have provided compression schemes and utilized machine learning to support the compression schemes.
Claim 15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Shen in view of Kumar, Fan, and Huang et al. US Patent Publication No. 2024/0121048 (“Huang”).
Regarding claim 15, Shen does not teach the apparatus according to claim 1, wherein the processor circuitry is further configured to receive sounding reference signal configuration transmitted by the network device; and transmit a sounding reference signal according to the sounding reference signal configuration.
Huang teaches a processor circuitry configured to receive sounding reference signal configuration transmitted by a network device; and transmit a sounding reference signal according to the sounding reference signal configuration (para. [0341] UE sends the corresponding SRS according to the configuration information of the SRS resource sent by the base station. para. [0347] UE receives the configuration information of M uplink SRS resources indicated by the base station, and uses N groups of SRS resources among the M uplink SRS resources to send the SRSs based on the configuration information). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shen with Huang’s disclosure. One of ordinary skill in the art would have been motivated to do so in order to have configured the apparatus for resource allocation and estimating channel quality.
Regarding claim 18, Shen does not teach the apparatus according to claim 15, wherein the sounded reference signal is used to obtain downlink channel estimation based on a channel state information reference signal by using channel reciprocity and via uplink channel estimation based on the sounding reference signal.
Huang teaches the sounded reference signal used to obtain downlink channel estimation based on a channel state information reference signal by using channel reciprocity and via uplink channel estimation based on the sounding reference signal (para. [0340] uplink SRS can also be used to obtain the Channel State Information (CSI). SRS can be used to estimate the uplink channel information of each UE. by using the reciprocity of the channel, the base station can also obtain the downlink channel state through the SRS. para. [0341] UE sends the corresponding SRS according to the configuration information of the SRS resource sent by the base station). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shen with Huang’s disclosure. One of ordinary skill in the art would have been motivated to do so in order to have configured the apparatus for resource allocation and estimating channel quality.
Allowable Subject Matter
Claims 13, 16, 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Additional Prior Art
The following prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Li et al. US Patent Publication No. 2024/0349082 (para. [0067] network device 102 may generate and send a UE capability inquiry 110 to the UE device 104 to request the UE device 104 to provide information about the UE's capabilities (e.g., ML capabilities and/or hardware capabilities). The UE device 104 may respond by generating and sending UE capability information 112 (e.g., indicating ML and/or hardware capabilities)
Kashyap et al. US Patent Publication No. 2024/0292198 (para. [0146] a discovery request is received from the UE A, the discovery request comprising an identification of an artificial intelligence and/or machine learning proximity service application (e.g. ProSe AI/ML Application ID) and an indication of AI/ML capabilities of the UE A)
Zhu et al. US Patent Publication No. 2022/0360973 (para. [0077] UE 602 may receive, from the base station 604, a UE capability request for an AI/ML procedure. The UE capability request may correspond to a UECapabilityEnquiry message. The UE capability request may also be received, at 608a, based on an indication from the core network 606 (e.g., via the base station 604). At 610a, the UE 602 may transmit a UE capability indication to the base station 604. Para. [0078] UE ML capability 612(3) may be based on one or more of the capability parameters indicated in the table 500).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JOSHUA JOO/Primary Examiner, Art Unit 2445
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